The present application relates to design of natural conversational systems and methods for measuring levels of mutual understanding between user and system.
A conversation system is a machine, such as a computer system that engages in a natural language conversation, typically with a human user. Typically, in conversational systems, dialog designers use a dialog scripting language (for example, VoiceXML) to encode a conversation between the user and the machine. While the goals of these scripting language are to help a dialog designer to create an engaging, robust end user interaction, the scripting language do so indirectly because the dialog designer has to ensure that the “script” he/she creates is valid and conforms to the programming model of the machine.
For the machine to have the natural language conversation with a human user, text analytic techniques, and conversational systems, the machine has to detect social actions the user is performing in order to determine user intent and to respond appropriately. Current solutions typically label utterances in conversational data in terms of their “dialogue acts” and use these labels to train statistical classifiers. Alternatively, some solutions (such as Dialog Act Markup in Several Layers (DAMSL)) label utterances in terms of whether they are repairs on previous or “antecedent” turns.